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20192025
most citedKoLA: Carefully Benchmarking World Knowledge of Large Language Models

24 citations · 136 across the 25 of their papers we have counts for

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12 papers · 1 filter

cs.CV2024

ENAT: Rethinking Spatial-temporal Interactions in Token-based Image Synthesis

Zanlin Ni, Yulin Wang, Renping Zhou +5

Recently, token-based generation have demonstrated their effectiveness in image synthesis. As a representative example, non-autoregressive Transformers (NATs) can generate decent-q…

cs.CV2024

AdaNAT: Exploring Adaptive Policy for Token-Based Image Generation

Zanlin Ni, Yulin Wang, Renping Zhou +6

Recent studies have demonstrated the effectiveness of token-based methods for visual content generation. As a representative work, non-autoregressive Transformers (NATs) are able t…

cs.CV2024

Revisiting Non-Autoregressive Transformers for Efficient Image Synthesis

Zanlin Ni, Yulin Wang, Renping Zhou +6

The field of image synthesis is currently flourishing due to the advancements in diffusion models. While diffusion models have been successful, their computational intensity has pr…

cs.CV2024★ 3 cited

LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images

Ruyi Xu, Yuan Yao, Zonghao Guo +7

Visual encoding constitutes the basis of large multimodal models (LMMs) in understanding the visual world. Conventional LMMs process images in fixed sizes and limited resolutions,…

cs.CV2023★ 1 cited

NExT-Chat: An LMM for Chat, Detection and Segmentation

Ao Zhang, Yuan Yao, Wei Ji +2

The development of large language models (LLMs) has greatly advanced the field of multimodal understanding, leading to the emergence of large multimodal models (LMMs). In order to…

cs.CV2023★ 4 cited

Reformulating Vision-Language Foundation Models and Datasets Towards Universal Multimodal Assistants

Tianyu Yu, Jinyi Hu, Yuan Yao +10

Recent Multimodal Large Language Models (MLLMs) exhibit impressive abilities to perceive images and follow open-ended instructions. The capabilities of MLLMs depend on two crucial…